Artificial intelligence has proven useful in finding patterns that humans cannot connect to otherwise. By shuffling through large amounts of data, AI can find the information you need in seconds and take action much faster than a real human. The problem is that AI relies on data provided by humans. Also, unlike humans, they cannot use empathy to tell when details are wrong.
This phenomenon is often referred to as “bias.” Incomplete data pools or algorithms can result in false positives or false positives that can impact your results. As hackers become more sophisticated each year, this bias could well pose a threat to cybersecurity.
Security threats may be overlooked in the future
Security threats can come from many directions. However, China, Russia, and India top the list of countries with the highest number of cybercriminals. This marks those countries as “dangerous” and means the AI defense system will keep most of its traffic in these countries.
The problem is that many countries that we consider low priorities have a steady but steady cybercrime problem. For example, Japan was previously considered a country with few cyberattacks and was considered a low priority. However, in 2012, cyberattacks in this country increased by her 8.3%, the highest in the past 15 years.
Humans know this, but AI is not yet fully trained to stay focused on these emerging countries. These can cause malware detection systems to overlook certain threats simply because they originate from places not originally thought to be a problem. Not updating databases and algorithms regularly can seriously jeopardize your cybersecurity efforts.
Hackers are learning to take advantage
As more companies rely on AI systems to detect threats, hackers may learn to exploit this flaw. Many people are starting to use VPNs to hide where they're coming from, choosing to appear in countries with lower crime rates. This can bias AI defense systems and not consider threats until it's too late.
The biggest problem here is that development teams may not even be aware that their systems have this kind of bias. Relying solely on AI systems to detect these threats can make it easier for malware to sneak into your system unnoticed. This is one of the main reasons why a mix of AI and human intelligence is recommended, as this type of collaboration helps minimize bias.
Increased risk of false positives
We've talked about how bias in AI can cause false negatives and incorrectly classify real threats as non-issues. However, the opposite can also happen. AI bias can cause false positives in your reports. In other words, you may find a problem where there is no problem.
This factor is often overlooked, especially now that many companies are using AI detection tools to reduce false positives. However, this can also lead to over-classification, especially since training on the data can make the detection system indistinguishable. This is very problematic because social media has made slang and slang so popular.
For example, those developing AI threat detection algorithms can associate slang or word abbreviations with phishing. This can result in important emails being classified as spam, causing delays in production. When an employee casually communicates through email or chat, phishing alerts can be triggered unnecessarily and tickets sent to her cybersecurity team.
This may seem like a good thing, since the system is “at least detecting it.” However, these false positives can distract attention from the actual threat. AI is biased and cannot distinguish between spam and actual communication between teams, putting an unnecessary burden on security departments. These are the moments that hackers are likely to take advantage of to launch attacks.
The ever-evolving landscape of cybersecurity
Perhaps the biggest threat of AI bias to cybersecurity is its inability to adapt to changing threat landscapes. As technology continues to develop faster than ever, cybersecurity threats are also increasing. Hacker attacks are also becoming more sophisticated, with more than 150,000 attacks occurring per hour. Some of these attacks have patterns, while others try to find new ways to circumvent security.
Training an AI model can take months or even years to successfully recognize new threats. This creates a blind spot in a company's security systems and can lead to further breaches as malware detection systems fail to detect attacks. This can be a big problem, especially when people rely on the fast capabilities of AI systems that reference large amounts of data. Human error can be a significant cybersecurity threat, but it can also depend on systems that are slow to change.
AI technology is continually evolving, especially when it comes to deep learning models. They are very opaque and complex, making them very difficult to manipulate. In this case, it can be very difficult to find where the source of the bias is and difficult to mitigate. Completely removing all bias is also not an ideal approach, as clear threats still exist. Therefore, it should not be ignored. This requires the use of hybrid models of human intelligence and AI. This prevents bias from growing out of control.
conclusion
Addressing bias in AI can be difficult, especially as the landscape is evolving in multiple areas. However, frequent testing can reduce bias and prevent attacks from spreading out of proportion. Prejudice cannot be completely eliminated, but controlled with appropriate human involvement.
